A comparison of Maxlike and Maxent for modelling species distributions

By Cory Merow, John A. Silander. 2014

120 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecological Modeling, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/c310c22cdcb6ca8b8d3ea9391df36db4c2cdb2b8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Brody Sandel
2. Jens‐Christian Svenning
3. Richard S. Condit
4. Mark P. Schildhauer
5. Brian J. McGill
6. Brian J. Enquist
7. Susan K. Wiser
8. Nathan J B Kraft

In Ecological Modeling

1. C. Violle
2. Brian J. Enquist
3. Brian J. McGill
4. Brody Sandel
5. Nathan J B Kraft
6. Richard S. Condit
7. P. M. Jørgensen
8. N. Morueta‐Holme

In Nature and Landscape Conservation

1. C. Violle
2. Brian J. Enquist
3. Brian J. McGill
4. Brody Sandel
5. Nathan J B Kraft
6. Jens‐Christian Svenning
7. Richard S. Condit
8. P. M. Jørgensen

Citations in the corpus

• The bien r package: A tool to access the Botanical Information and Ecology Network (BIEN) database (2017, methodology): “Maxent model building generally followed the recommendations outlined in (Merow, Cory, & Silander, 2014; Merow, Cory, Smith, & Silander, 2013).”
• Climate change, genetic markers and species distribution modelling (2015, unknown): “Merow & Silander (2014) showed that the underlying models of MaxEnt and MaxLike are similar to one another, and that the slope coefficients from both are comparable to those generated by a generalized linear model (GLM) applied to presence/absence data.”